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. 2014 Jul;42(Web Server issue):W137-46.
doi: 10.1093/nar/gku412. Epub 2014 Jun 3.

DiseaseConnect: a comprehensive web server for mechanism-based disease-disease connections

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DiseaseConnect: a comprehensive web server for mechanism-based disease-disease connections

Chun-Chi Liu et al. Nucleic Acids Res. 2014 Jul.

Abstract

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease-disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug-disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments.

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Figures

Figure 1.
Figure 1.
Constructing the mechanism-based disease–disease network based on the GWAS/OMIM/DEG records. (A) We combine the disease–gene connections derived from GWAS, OMIM and DEG records to build a comprehensive disease–gene network (D1, D2, D3 and D4 indicate diseases and G1, G2, G3 and G4 indicate genes). For each disease pair, we calculate the hypergeometric P-value to assess the significance of the number of genes involved in both diseases. We also add drug–treatment relations to complement further the database. (B) In the disease–disease network, when users click the edge between disease D1 and D2, the web server generates the detailed network of disease D1 and D2, including DEG/GWAS/OMIM/GeneRIF/GeneWays disease–gene relations and drug treatment/target relations.
Figure 2.
Figure 2.
Illustrations of the gene, disease and disease connection views in the DiseaseConnect web server. (A) Gene view: user inputs STAT3 to search for all diseases related to this gene based on the GWAS/OMIM/DEG records. The web server generates a disease–disease network on the diseases associated with STAT3. In this network, the disease–disease connection indicates that two diseases involve a significant number of common genes based on the GWAS/OMIM/DEG records. (B) Disease view: user inputs arthritis, and then the web server displays a disease–disease network (using the diseases connecting arthritis) in the left panel and the disease–gene–drug network (using the arthritis-related drugs and genes) in the right panel. The disease–gene–drug network of arthritis includes the following molecules: (i) genes associated with arthritis, (ii) the drugs targeting the disease-related genes and (iii) the drugs treating arthritis. (C) Disease connection view: user inputs arthritis and Crohn disease, and then the web server displays a network connecting the two diseases. Users can enable the drug option to show the drug–disease treatment relations. In each view, the web server automatically adjusts the P-value threshold to maintain the size of the network to less than 100 nodes.
Figure 3.
Figure 3.
Diseases pairs sharing more involved genes are more likely to have disease comorbidity. The statistical significance (P-value) of the connection between two diseases is assessed by a hypergeometric test on shared genes derived from each individual data source of GWAS, OMIM, DEG, GeneRIF and GeneWays. We used various P-value thresholds (x axis) to select significant disease–disease connections for each data source, and then calculated the fraction (y axis) of those disease–disease connections that overlap with the disease comorbidity connections.
Figure 4.
Figure 4.
Disease pairs sharing more genes are more likely to have the same drug treatment. The statistical significance (P-value) of the connection between two diseases is assessed by a hypergeometric test on shared genes derived from each individual data source of GWAS, OMIM, DEG, GeneRIF and GeneWays. We used various P-value thresholds (x axis) to select significant disease–disease connections for each data source, and then calculated the fraction (y axis) of the disease–disease connections in which both diseases can be treated by the same drug(s).
Figure 5.
Figure 5.
Disease module 109 is enriched of the diseases that can be treated by fludarabine. There are nine total diseases, and fludarabine can treat five of those diseases. Although multiple myeloma and acute lymphocytic leukemia are currently not treated with fludarabine, we found strong literature evidence that supports this potential treatment.
Figure 6.
Figure 6.
Disease connections with drug treatment implications. (A) PSMB5 is a DEG for both hemorrhagic disorders and multiple myeloma, and PSMB5 also has GeneRIF association with multiple myeloma. (B) Arthritis and Crohn disease are associated with TNF based on the GWAS and GeneRIF records. Thalidomide is an immunomodulatory drug that targets TNF and can treat both arthritis and Crohn diseases. Adalimumab also targets TNF and can treat arthritis, suggesting its potential treatment of Crohn disease.

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